Remote 3D ML/AI & MDO Engineer - Digital Twins

United States Digital Space LLC; United States; Remote; USD 110,000 - 165,000; Full time

Benefits offered by this job

Company shareholder option

Flexible remote work policy

Robust pension program

Learning & development resources

Job summary

United States Digital Space LLC seeks an experienced Research and Development Engineer to drive innovative 3D machine learning applications, surrogate models, and digital twins for turbomachinery MDO. You will shape data-driven engineering methods and advance design optimization in the gas turbine sector.

You will develop ML design tools, validate surrogate models, automate data workflows, and integrate models into optimization loops, collaborating with interdisciplinary teams to deliver robust,

Qualifications

• Advanced degree in Mechanical/Aerospace/CS/Applied Math or related field preferred.
• Strong programming skills in Python, C++, Java, Git, and Linux.
• ML model development, validation, deployment, and data-science tooling experience.

Responsibilities

• Develop 3D ML-based design tools for aerodynamics, heat transfer, and structural integrity.
• Create surrogate models and digital twins for MDO workflows.
• Automate data preparation, model training, validation, and deployment pipelines.
• Integrate ML models into optimization loops and decision-support processes.
• Collaborate with interdisciplinary teams to deliver data-driven solutions.

Skills

Python

C++

Java

Git

Linux

ML frameworks

Education

Master's degree or PhD preferred

Tools

NumPy

pandas

PyTorch

TensorFlow

scikit-learn

Job description

United States Digital Space LLC seeks an experienced Research and Development Engineer to drive innovative 3D machine learning applications, surrogate models, and digital twins for turbomachinery MDO. You will shape data-driven engineering methods and advance design optimization in the gas turbine sector.

You will develop ML design tools, validate surrogate models, automate data workflows, and integrate models into optimization loops, collaborating with interdisciplinary teams to deliver robust,